%matplotlib inline

import math
import torch
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

from startorch import sequence as seq
from startorch.utils.plot import hist_sequence, plot_sequence

plt.style.use("bmh")
plt.rcParams["figure.figsize"] = (16, 5)

generator = seq.RandAsinhUniform(low=-1000.0, high=1000.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandAsinhUniformSequenceGenerator(low=-1000.0, high=1000.0, feature_size=(1,))

fig = hist_sequence(generator, bins=500)
fig = hist_sequence(generator, bins=500, scale='asinh')

generator = seq.RandLogUniform(low=0.001, high=1000.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandLogUniformSequenceGenerator(low=0.001, high=1000.0, feature_size=(1,))

fig = hist_sequence(generator, bins=500)
fig = hist_sequence(generator, bins=500, scale='log10')

generator = seq.RandTruncCauchy(loc=0.0, scale=1.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandTruncCauchySequenceGenerator(loc=0.0, scale=1.0, min_value=-2.0, max_value=2.0, feature_size=(1,))

fig = hist_sequence(generator, bins=500)

generator = seq.RandTruncExponential(rate=1.0, max_value=5.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandTruncExponentialSequenceGenerator(rate=1.0, max_value=5.0, feature_size=(1,))

fig = hist_sequence(generator, bins=500)

generator = seq.RandTruncNormal(mean=1.0, std=2.0, min_value=-2.0, max_value=4.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandTruncNormalSequenceGenerator(mean=1.0, std=2.0, min_value=-2.0, max_value=4.0, feature_size=(1,))

fig = hist_sequence(generator, bins=500)

generator = seq.RandTruncHalfCauchy(scale=1.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandTruncHalfCauchySequenceGenerator(scale=1.0, max_value=4.0, feature_size=(1,))

fig = hist_sequence(generator, bins=500)

generator = seq.RandTruncHalfNormal(std=1.0, max_value=2.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandTruncHalfNormalSequenceGenerator(std=1.0, max_value=2.0, feature_size=(1,))

fig = hist_sequence(generator, bins=500)

generator = seq.RandTruncLogNormal(mean=0.0, std=1.0, max_value=2.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandTruncLogNormalSequenceGenerator(mean=0.0, std=1.0, min_value=0.0, max_value=2.0, feature_size=(1,))

fig = hist_sequence(generator, bins=500)

generator = seq.RandUniform(low=-5, high=5)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandUniformSequenceGenerator(low=-5.0, high=5.0, feature_size=(1,))

fig = hist_sequence(generator, bins=500)

generator = seq.RandExponential(rate=0.1)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandExponentialSequenceGenerator(rate=0.1, feature_size=(1,))

fig = hist_sequence(generator, bins=500, range=(0, 5))

generator = seq.RandHalfCauchy(scale=1.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandHalfCauchySequenceGenerator(scale=1.0, feature_size=(1,))

fig = hist_sequence(generator, bins=500, range=(0, 8))

generator = seq.RandHalfNormal(std=1.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandHalfNormalSequenceGenerator(std=1.0, feature_size=(1,))

fig = hist_sequence(generator, bins=500, range=(0, 3))

generator = seq.RandLogNormal(mean=0.0, std=1.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandLogNormalSequenceGenerator(mean=0.0, std=1.0, feature_size=(1,))

fig = hist_sequence(generator, bins=500, range=(0, 10))

generator = seq.RandCauchy(loc=0.0, scale=1.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandCauchySequenceGenerator(loc=0.0, scale=1.0, feature_size=(1,))

fig = hist_sequence(generator, bins=500, range=(-10, 10))

generator = seq.RandNormal(mean=0.0, std=1.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandNormalSequenceGenerator(mean=0.0, std=1.0, feature_size=(1,))

fig = hist_sequence(generator, bins=500, range=(-4, 4))

generator = seq.RandWienerProcess()
print(generator)
fig = plot_sequence(generator, batch_size=10)

RandWienerProcessSequenceGenerator(time_step_size=1.0)

fig = hist_sequence(generator, bins=500, range=(-100.0, 100.0))

generator = seq.RandPoisson(rate=5.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandPoissonSequenceGenerator(rate=5.0, feature_size=(1,))

fig = hist_sequence(generator, bins=100, range=(0, 10))

generator = seq.Multinomial.create_linear_weights(num_categories=50)
print(generator)
fig = plot_sequence(generator, batch_size=4)

MultinomialSequenceGenerator(num_categories=50, feature_size=(1,))

fig = hist_sequence(generator, bins=50)

generator = seq.Multinomial.create_exp_weights(num_categories=50)
print(generator)
fig = plot_sequence(generator, batch_size=4)

MultinomialSequenceGenerator(num_categories=50, feature_size=(1,))

fig = hist_sequence(generator, bins=50)

generator = seq.Multinomial.create_uniform_weights(num_categories=50)
print(generator)
fig = plot_sequence(generator, batch_size=4)

MultinomialSequenceGenerator(num_categories=50, feature_size=(1,))

fig = hist_sequence(generator, bins=50)

generator = seq.UniformCategorical(num_categories=50)
print(generator)
fig = plot_sequence(generator, batch_size=4)

UniformCategoricalSequenceGenerator(num_categories=50, feature_size=())

fig = hist_sequence(generator, bins=50)

generator = seq.RandInt(low=5, high=50)
print(generator)
fig = plot_sequence(generator, batch_size=4)

RandIntSequenceGenerator(low=5, high=50, feature_size=())

fig = hist_sequence(generator, bins=45)

Table of content¶

Continuous univariate supported on a bounded interval¶

Asinh-uniform¶

Log-uniform¶

Truncated Cauchy¶

Truncated Exponential¶

Truncated Normal¶

Truncated half-Cauchy¶

Truncated half-Normal¶

Truncated log-Normal¶

Uniform¶

Continuous univariate supported on a semi-inifinte interval¶

Exponential¶

Half-Cauchy¶

Half-Normal¶

Log-Normal¶

Continuous univariate supported on an inifinte interval¶

Cauchy¶

Normal¶

Wiener process¶

Discrete univariate supported on a semi-infinite support¶

Poisson¶

Discrete univariate supported on a finite support¶

Multinomial¶

Uniform categorical¶

Uniform integer¶